# doc-cache created by Octave 10.1.0
# name: cache
# type: cell
# rows: 3
# columns: 12
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 8
Contents


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1119
 
 T320: Removal of Physiolgical Artifacts from NIRS
 ---------------------------------------------
	adaptPulsremove.m     removes the Pulse influence from NIRS signals using calcInfluence.m
 	calcInfluence.m       calculates the unknown influence of the Pulse-Noise using the LMS algorithm
 	remNoiseTF.m          removes respiration and MayerWave artefacts by the transfer function approach
   remNoiseICA.m         removes respiration and MayerWave artefacts by ICA approach
 	remNoiseCAR.m         removes respiration and MayerWave artefacts by CAR approach

 Additional files
 -------------------
   calcNIRSspectra.m     calculates the Spectrum of the NIRS signal
   calcBPlin.m           calculates the linear interpolated BPdia or BPsys signal using
   sysdetect.m           calculates fiducially points of systolic BP
   diadetect.m           calculates fiducially points of diatolic BP
   Illustration_multichannel_spectra.m   uses the calcNIRSspectra function to calculate and illustrates the [(de)oxy-Hb] spectra
                                       of each used NIRS channel from a 3*11 measurement grid



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
 
 T320: Removal of Physiolgical Artifacts from NIRS
 -----------------------...



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 33
Illustration_multichannel_spectra


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 122
undocumented function: [rOxy, rDeoxy] = Illustration_multichannel_spectra (ChNr, oxy_Data, deoxy_Data, fs, dispFreq, ExCh)


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
undocumented function: [rOxy, rDeoxy] = Illustration_multichannel_spectra (Ch...



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 16
adaptPulseremove


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 490
 adaptPulseremove removes the influence of the Pulse-Signal from the
 [(de)oxy-Hb] signal

 [cleanSignal]=adaptPulseremove(dirtySignal,Noise,fs)

 Input:
   dirtySignal     ... dirty signal (either [oxy-Hb] or [deoxy-Hb]) with pulse influence
   Noise           ... artificial Noise signal (either BP signal or signal from a
                       fingerPulse sensor
   fs              ... Sampling frequency    

 Output:
   cleanSignal     ... clean signal without influence



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
 adaptPulseremove removes the influence of the Pulse-Signal from the
 [(de)o...



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 9
calcBPlin


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 401
 calcBPlin calculates the linear interpolated BPdia or BPsys signal and is partially based
 on the calcHR function written by Clemens Brunner.


 [linBP] = calcBPlin(signal,fs,typ)

 Input:
   signal   ... Raw continuous BP signal
   fs       ... Sampling frequency
   typ      ... [1] systolic BP
            ... [2] diastolic BP


 Output:
   linBP    ... linear interpolated BPdia or BPsys signal




# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
 calcBPlin calculates the linear interpolated BPdia or BPsys signal and is pa...



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 13
calcInfluence


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 455
 calcInfluence calculates the unknown influence of the Puls-Noise using
 the LMS algorithm

 [influence] = calcInfluence(dirtySignal, Noise)

 Input:
   dirtySignal     ... dirty signal (either [oxy-Hb] or [deoxy-Hb]) with puls influence
   Noise           ... preprocessed artificial Noise signal (either BP signal or signal from a
                       fingerpuls sensor
     

 Output:
   influence       ... calculated unknown influence



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
 calcInfluence calculates the unknown influence of the Puls-Noise using
 the...



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 15
calcNIRSspectra


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 257
 calcNIRSspectra calculates the Spectrum of the NIRS signal

 r=calcNIRSspectra(signal,fs)

 Input:
   signal  ... NIRS signal (either [oxy-Hb] or [deoxy-Hb] 
   fs      ... Sampling frequency    

 Output:
   r       ... structure containing the spectrum.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 59
 calcNIRSspectra calculates the Spectrum of the NIRS signal



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 14
demo_t320_nirs


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 119
 DEMO_t320_nirs 
 Demonstrates Different Approaches for the 
 Removal of Physiological Artefacts  from NIRS Signals



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
 DEMO_t320_nirs 
 Demonstrates Different Approaches for the 
 Removal of Ph...



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 9
diadetect


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 229
 diadetect - detection of Diastolic_BP points

   HDR = diasysdetect(signal,Fs)

 Input:
   signal      ... BP signal data 
   fs          ... Sampling frequency 

 Output:
   HDR.EVENT   ... fiducially points of diastolic BP	





# name: <cell-element>
# type: sq_string
# elements: 1
# length: 45
 diadetect - detection of Diastolic_BP points



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 11
remNoiseCAR


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 481

 remNoiseCAR removes respiration an blood pressure related noise from
 [(de)oxy-Hb] signals by using CAR. 

   [cleanSignals] = remNoiseCAR(Signals,fs,ExCh)

 Input:
   oxy_signals      ... Oxy-Signals with noise
   deoxy_signals    ... Deoxy-Signals with noise
   ExCh             ... Channels not used


 Output:
   cleanOxysignals    ... corrected Oxy-signal without influence of noise
   cleanDeoxysignals  ... corrected Deoxy-signal without influence of noise



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80

 remNoiseCAR removes respiration an blood pressure related noise from
 [(d...



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 11
remNoiseICA


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1386

 remNoiseICA removes respiration an blood pressure related noise from
 [(de)oxy-Hb] signals by using ICA. The [(de)oxy-Hb] signal is decomposed 
 into independent components (ICs) via SOBI ICA [1]. The coherence between 
 each IC and the noise signals is then calculated. ICs for which the coherence 
 with one of the artefact signals is higher than the mean of all the coherence 
 scores with that artefact signal plus 1 standard deviation are flagged for 
 removal. 

 The code uses the runica function from:
 Makeig, Scott et al. "EEGLAB: ICA Toolbox for Psychophysiological Research". 
 WWW Site, Swartz Center for Computational Neuroscience, Institute of Neural
 Computation, University of San Diego California
 <www.sccn.ucsd.edu/eeglab/>, 2000.

 Please be sure to have included

   [cleanSignals]=remNoiseICA( Signals,bpNoise,respNoise,fs,ExCh)

 Input:
   Signals    ... Signals with noise (either [oxy-Hb] or [deoxy-Hb])
   bpNoise    ... Noise signal BP
   respNoise  ... Noise signal respiration
   fs         ... Sampling frequency
   ExCh       ... Channels not used


 Output:
   cleanSignals  ... corrected signal without influence of noise

[1] Belouchrani A, Abed-Meraim K, Cardoso J, Moulines E. A blind source
    seperation technique using second-order statistics. IEEE Transactions 
    on signal processing, 45(2): 434-444, 1997



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80

 remNoiseICA removes respiration an blood pressure related noise from
 [(d...



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 10
remNoiseTF


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1053

 remNoiseTF removes respiration an blood pressure related noise from
 [(de)oxy-Hb] signals by using a transfer function model [1,2]. For a detailed
 description of the model see [3].
  
   [corrSignal]=remNoiseTF(signal,noise,fs,shift)

 Input:
   signal     ... signal with noise (either [oxy-Hb] or [deoxy-Hb])
   noise      ... noise signal from a different source (respiration, BPdia, HR, ...    
   fs         ... sampling frequency
 
 Optional input parameter: 
   windowlength ... length of one segment for the correction, default=240 seconds

 Output:
   corrSignal:  ... corrected signal without influence of noise

[1] Priestley, M.B., 1981. Spectral Analysis and Time Series. Vol. 1 and 2.
    Academic Press, London, pp. 671.
[2] Wei, W.W.S., 1990. Time Series Analysis; Univariate and Multivariate Methods. Addison Wesley, New York, pp. 289.
[3] Florian G, Stancak A, Pfurtscheller G. Cardiac response induced by
    voluntary selfpaced finger movement. International Journal of Psychophysiology, 28: 273-283, 1998.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80

 remNoiseTF removes respiration an blood pressure related noise from
 [(de...



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 9
sysdetect


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 231
 SYSDETECT - detection of SYS_BP_points

   [HDR] = sysdetect(signal,fs)

 Input:
   signal          ... BP signal data 
   fs              ... Sampling frequency 

 Output:
   HDR.EVENT.pos   ... fiducially points of systolic BP




# name: <cell-element>
# type: sq_string
# elements: 1
# length: 39
 SYSDETECT - detection of SYS_BP_points





